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What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

Published online by Cambridge University Press:  30 October 2001

Peter C.R. Lane ,
Fernand Gobet  and
Peter C-H. Cheng
Peter C.R. Lane
Affiliation:
ESRC Centre for Research in Development, Instruction and Training, School of Psychology, University of Nottingham, Nottingham NG7 2RD, United [email protected]@[email protected] www.psychology.nottingham.ac.uk/staff/Peter.Lane www.psychology.nottingham.ac.uk/staff/Fernand.Gobet www.psychology.nottingham.ac.uk/staff/Peter.Cheng
Fernand Gobet
Affiliation:
ESRC Centre for Research in Development, Instruction and Training, School of Psychology, University of Nottingham, Nottingham NG7 2RD, United [email protected]@[email protected] www.psychology.nottingham.ac.uk/staff/Peter.Lane www.psychology.nottingham.ac.uk/staff/Fernand.Gobet www.psychology.nottingham.ac.uk/staff/Peter.Cheng
Peter C-H. Cheng
Affiliation:
ESRC Centre for Research in Development, Instruction and Training, School of Psychology, University of Nottingham, Nottingham NG7 2RD, United [email protected]@[email protected] www.psychology.nottingham.ac.uk/staff/Peter.Lane www.psychology.nottingham.ac.uk/staff/Fernand.Gobet www.psychology.nottingham.ac.uk/staff/Peter.Cheng
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Abstract

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.

Type
Brief Report
Information
Behavioral and Brain Sciences , Volume 24 , Issue 1 , February 2001 , pp. 128 - 129
Copyright
© 2001 Cambridge University Press

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